Single underwater image often suffer from severe quality degradation and field-of-view limitation due to the underwater light propagation characteristics and the viewing range of camera equipment. To address these challenges, we propose a underwater scene clarity reconstruction framework called USCR, which comprises a multilayer information fusion (MIF) method for underwater image enhancement (UIE) and a self-organized stitching (SOS) method for image stitching. First, MIF corrects color distortion, enhances contrast, and highlights image detail information through a minimally attenuated channel guided color correction strategy and a gradient weight fusion strategy. Subsequently, SOS is applied to stitch the enhanced underwater images, which utilizes a homography matrix to initially stitch the image sequence, and further employs a pixel blending strategy based on boundary distance weighting for boundary pixel fusion to the initial stitch image, aiming to ensure a homogeneous transition of the stitch region. Our reconstructed underwater scenes are characterized by visual clarity and a wide field-of-view. Extensive qualitative and quantitative experimental validations show that USCR outperforms the state-of-the-art methods in underwater visual reconstruction task.

An open source Linux distribution often undergoes version upgrades and migrations, which is prone to incompatibility issues especially when it comes to large-scale software changes. Although differential testing has been widely used in software testing, it is still challenging to apply it for detecting such incompatibilities in the context of industrial settings. In this paper, we report our experience in leveraging LLMs to address the challenges faced by the Linux distribution community. Specifically, we develop an LLM-based differential testing method called Versify to assist maintainers of Linux distributions in locating incompatibilities during version upgrades and migrations. Its trial operation period within the Linux distribution community shows that it uncovered 8,489 instances of differing behavior, of which 644 were prioritized for attention by developers. After deduplication and filtering, 39 unique compatibility reports were identified. Feedback from Linux distributions developers indicates that our reports have provided valuable recommendations for package selection in future OS releases.

Kata Containers is a secure container runtime with lightweight virtual machines and a customized Linux kernel optimized for cloud-native workloads, which is important for cloud-native systems. Fuzzing is a widely-used technique for detecting kernel vulnerability. However, current kernel fuzzers can't be simply applied to kernels in cloud-native environments because of the discrepancies between test and actual deployment scenarios. This paper introduces DragonRadar, a kernel fuzzing tool adapted for Kata Containers, which aligns the testing environment with cloud deployment realities. We extend to support kernel fuzzing in cloud-native environments by integrating Syzkaller's capability with a lightweight virtual machine manager called Dragonball. The evaluation shows that DragonRadar effectively identifies 25 kernel vulnerabilities in the mainline Linux kernel used in the Kata Containers environment, while maintaining code coverage similar to vanilla Syzkaller. DragonRadar is available at https://github.com/TOBESTONG//DragonRadar.

Underwater image captioning bridges the gap between visual perception and semantic understanding of underwater scenes, playing a crucial role in applications such as ocean geoscience and underwater remote sensing. Despite progress in this field, limitations remain in achieving accurate underwater image captioning. The main limitations are: (a) the underestimation of basic sketch features in underwater image captioning, and (b) insufficient consideration of the impact of scale differences in underwater objects. To overcome these limitations, we propose underwater image captioning with AquaSketch enhanced cross-scale information fusion. Our novel contributions are twofold: (a) A novel AquaSketch (i.e., aqua sketch) enhancement method is developed to reduce the impact of underwater image distortion on scene understanding, while enhancing both detailed and background information; and (b) A top-down dual-branch pyramid for cross-scale information fusion is proposed. This architecture fuses multi-scale feature information from two branches through an attention-based feature fusion structure, performing cross-scale fusion in a top-down manner. The resulting pyramid fusion features offer a comprehensive representation of underwater object information. Collectively, these contributions facilitate the generation of accurate and comprehensive underwater image captions. Experimental evaluations on three datasets demonstrate that our proposed underwater image captioning model achieves state-of-the-art performance in the field.

Directed grey-box fuzzing is a widely used automatic testing technique that has helped developers test specific code space in the target program. Although many directed fuzzers are designed to test the Linux kernel, challenges still remain due to the complexity of industrial requirements and deployment environments. In this paper, we collaborate with developers from Alibaba and the OpenAnolis community to conduct an industry practice of directed kernel fuzzing for open-source Linux distribution. We highlight typical challenges in deploying directed kernel fuzzing, including target-related kernel configuration options being disabled, unrelated initial seeds limiting fuzzing startup performance, no support for kernel feature interface fuzzing, independent fuzzer execution limiting fuzzing effectiveness, much manual work to triage and analyze crashes, and hard to integrate into the existing fuzzing framework. We provide solutions to these challenges, which allowed us to discover 11 previously unknown kernel bugs related to cloud-native features, io_uring, and other components in the OpenAnolis Linux distribution.

Underwater images often reveal color distortion and poor visibility due to light propagation in water being affected by the selective absorption and scattering of suspended particles. This letter presents an efficient color channel transfer (CCT) method that largely restores color distortion and improves visibility of underwater images. Any captured underwater image with at least one color channel is highly attenuated in real underwater imaging. To compensate for the loss of information in the attenuated channel, the CCT transfers the degraded image to the CIELab color space and compensates for the loss of information in the degraded image by adjusting luminance and chrominance. The reference values of the color transfer image are statistically calculated from many high-quality images to ensure a relatively balanced color distribution. Extensive experiments on three underwater image datasets show that after applying our CCT, the enhancement method leads to satisfactory results in both metric scores and runtimes.

Underwater images normally suffer from visual degradation issues such as color deviations, low contrasts, and blurred details. Recently, numerous underwater image enhancement algorithms have been proposed to address these issues. However, constrained by underwater conditions, acquiring non-underwater images and depth maps for underwater images is often challenging. This limitation significantly hampers the performance of data driven-based methods and physical model-based methods. Additionally, existing physical model-based methods typically require manual parameter settings, which tend to be bruteforce and insufficient to effectively address the diverse underwater scenes. To overcome these limitations, this paper presents a comprehensive underwater image enhancement framework comprising three phases: metamergence (i.e., meta submergence), metalief (i.e., meta relief), and metaebb (i.e., meta ebb). These phases are dedicated to virtual underwater image synthesis, underwater image depth map estimation, and the configuration of state-of-the-art physical models for underwater image enhancement by reinforcement learning, separately. While the three phases are trained separately, the former phase provides the necessary data for training the latter. We refer to the overall three phases as metalantis (i.e., meta Atlantis) because its training processes, involving variations from submergence via relief to ebb over indoor scenes, mimic the virtual variations of Atlantis. The metalantis framework empowers state-of-the-art physical models of underwater imaging through reinforcement learning with virtually generated data. The well-trained metalantis framework can take an underwater image as the sole input, process it into virtual representations, and finally enhance it. Comprehensive qualitative and quantitative empirical evaluations validate that our metalantis framework outperforms state-of-the-art underwater image enhancement methods. We release our code at https://gitee.com/wanghaoupc/Metalantis_UIE.

The task of hyperspectral images (HSIs) target detection is to identify whether the target spectral sequences present in the HSI. Recently, the topic of representation models has received much interest in hyperspectral target detection. The performance of representation models depends on whether the corresponding dictionary and sparse matrix can correctly recover the original spectrum. Therefore, the background dictionary of these models should contain the spectra of all classes except the target spectrum; i.e., the dictionary should be overcomplete. However, most representation models cannot satisfy this condition. Moreover, due to the potentially large spectral similarity between the target and the background, representation models perform poorly in background suppression. Aiming to solve these issues, a novel adaptively dictionary construction (ADC) strategy with background suppression sparse representation (BSSR) module is proposed in this letter, called adaptively dictionary construction for target detection (ADCTD). Specifically, the proposed ADC is adopted to segment the HSI into superpixels consisting of pixels with similar spectra. This process can be considered as an unsupervised coarse classification process, which can construct an overcomplete background dictionary. In addition, the BSSR is adopted to improve the separation of the target and background by a linear function. Experiments on three datasets demonstrate the superiority of the proposed ADCTD.

In recent years, multimodal sentiment analysis (MSA) has attracted more and more interest, which aims to predict the sentiment polarity expressed in a video. Existing methods typically 1) treat three modal features (textual, acoustic, visual) equally, without distinguishing the importance of different modalities; and 2) split the video into frames, leading to missing the global acoustic information. In this paper, we propose a global Acoustic feature enhanced Modal-Order-Aware network (AMOA) to address these problems. Firstly, a modal-order-aware network is designed to obtain the multimodal fusion feature. This network integrates the three modalities in a certain order, which makes the modality at the core position matter more. Then, we introduce the global acoustic feature of the whole video into our model. Since the global acoustic feature and multimodal fusion feature originally reside in their own spaces, contrastive learning is further employed to align them before concatenation. Experiments on two public datasets show that our model outperforms the state-of-the-art models. In addition, we also generalize our model to the sentiment with more complex semantics, such as sarcasm detection. Our model also achieves state-of-the-art performance on a widely used sarcasm dataset.

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.

Multi-label image classification (MLIC) is a more challenging task compared with single-label image classification due to multiple concepts targets, and complex visual relationships should be formulated. Convolutional Neural Network (CNN) and Visual Transformer (ViT) have shown superior performance in local and global feature representations, respectively. However, the interactions between local and global features are neglected in current works. To further formulate the critical interactions, this paper designs a Unified Feature Interaction (UFI) framework, aiming to integrate the selected local features with global features based on CNN and ViT, simultaneously. The proposed UFI includes two key modules: Class-Related Feature Selection (CRFS) and Feature Interaction Attention (FIA) modules. Specifically, according to the activation map, CRFS selects target regions by the preliminary calculation of predicted scores. FIA enables the significant local-global feature interaction based on the selected target regions and whole image. We initially attempted to interact with local and global features for multi-label image classification. UFI provides a stable improvement over the baseline and produces a new state-of-the-art result on MS-COCO and VOC2007.

Nowadays advanced facial manipulation techniques produce deepfake videos more realistically, which makes deepfake detection more difficult. To capture subtle and intricate artifacts, recent works attempt to enhance low-level textural information by attention-based framework. However, these methods require complex simulated data or extra supervision. Highly dependent on training settings, these methods not only have high training costs but also are prone to overfitting. To address this issue, we propose a novel perspective of deepfake detection via so-called prior-attention. Specifically, we introduce prior textural information, such as edge and noise, to model the attention maps explicitly. Benefiting from these natural “attention maps”, our model significantly enhances discriminative information without additional supervision. Furthermore, we design a Feature Abstraction Block (FAB) to facilitate cross-layer features interaction and insert it into distinct layers of CNN to detect the inconsistencies at multiple spatial levels. Extensive experiments demonstrate that our method achieves performance comparable to state-of-the-art methods.

The utilization of the unlabeled data provides a beneficial attempt for improving the generalization ability of the convolutional neural network (CNN) model, just as what is applied in person re-identification task. Different from that, multi-label image classification aims to predict multiple labels for each given image. The unlabeled data should be properly assigned multiple labels for regularizing the training process of CNN model. To make full use of the unlabeled data, this paper proposes a soft pseudo labeling (SPL) method for multi-label image classification. Specifically, the unlabeled samples are first generated by DCGAN and WGAN-GP. Then, the virtual multiple labels of the generated unlabeled samples are assigned based on an initial confidence value by SoftMax function. Finally, both the generated samples and original training samples are fed into the network as input, in order to learn a CNN model with stronger generalization ability. On three public multi-label image classification datasets (i.e., WIDER-Attribute, NUS-WIDE and MS-COCO), SPL provides a stable improvement over the baseline and produces a competitive performance compared with some existing multi-label image classification methods.